Dong Qinghai, Wang Bingnan, Xiang Maosheng, Wang Zhongbin, Wang Yachao, Song Chong
National Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel). 2022 Mar 28;22(7):2596. doi: 10.3390/s22072596.
Circular synthetic aperture radar (CSAR), which can observe the region of interest for a long time and from multiple angles, offers the opportunity for moving-target detection (MTD). However, traditional MTD methods cannot effectively solve the problem of high probability of false alarm (PFA) caused by strong clutter. To mitigate this, a novel, three-step scheme combining clutter background extraction, multichannel clutter suppression, and the degree of linear consistency of radial velocity interferometric phase (DLRVP) test is proposed. In the first step, the spatial similarity of the scatterers and the correlation between sub-aperture images are fused to extract the strong clutter mask prior to clutter suppression. In the second step, using the data remaining after elimination of the background clutter in Step 1, an amplitude-based detector with higher processing gain is utilized to detect potential moving targets. In the third step, a novel test model based on DLRVP is proposed to further reduce the PFA caused by isolated strong scatterers. After the above processing, almost all false alarms are excluded. Measured data verified that the PFA of the proposed method is only 20% that of the comparison method, with improved detection of slow and weakly moving targets and with better robustness.
圆合成孔径雷达（CSAR）能够长时间从多个角度观测感兴趣区域，为动目标检测（MTD）提供了机会。然而，传统的MTD方法无法有效解决强杂波导致的高虚警概率（PFA）问题。为缓解这一问题，提出了一种新颖的三步方案，该方案结合了杂波背景提取、多通道杂波抑制以及径向速度干涉相位线性一致性程度（DLRVP）测试。第一步，在杂波抑制之前，融合散射体的空间相似性和子孔径图像之间的相关性，以提取强杂波掩膜。第二步，利用第一步中消除背景杂波后剩余的数据，采用具有更高处理增益的基于幅度的检测器来检测潜在的动目标。第三步，提出了一种基于DLRVP的新颖测试模型，以进一步降低由孤立强散射体引起的PFA。经过上述处理，几乎所有虚警都被排除。实测数据验证了所提方法的PFA仅为比较方法的20%，对慢速和弱动目标的检测有所改进，且具有更好的稳健性。
